A framework for quantifying net benefits of alternative prognostic models

Statistics in Medicine

Volumn 31, Issue 2, 2012, Pages 114-130

  Rapsomaniki, Eleni ^a   White, Ian R ^b   Wood, Angela M ^a   Thompson, Simon G ^a,b   Tipping, R W ^a   Ford, C E ^a   Simpson, L M ^a   Folsom, A R ^a,a   Chambless, L E ^a   Panagiotakos, D B ^a   Pitsavos, C ^a   Chrysohoou, C ^a   Stefanadis, C ^a   Knuiman, M ^a   Whincup, P H ^a   Wannamethee, S G ^a   Morris, R W ^a   Kiechl, S ^a   Willeit, J ^a   Oberhollenzer, F ^a   Mayr, A ^a   Wald, N ^a   Lawlor, D A ^a   Yarnell, J W ^a   Gallacher, J ^a   Casiglia, E ^a   Tikhonoff, V ^a   Nietert, P J ^a   Sutherland, S E ^a   Bachman, D L ^a   Keil, J E ^a   Cushman, M ^a,a   Tracy, R ^a   Tybjaerg Hansen A ^a   Nordestgaard, B G ^a   Frikke Schmidt, R ^a   Giampaoli, S ^a   Palmieri, L ^a   Panico, S ^a   Vanuzzo, D ^a   Pilotto, L ^a   Gomez de la Camara A ^a   Gomez Gerique J A ^a   Simons, L ^a   McCallum, J ^a   Friedlander, Y ^a   Lee, A J ^a   Taylor, J ^a   Guralnik, J M ^a,a   Wallace, R ^a   Blazer, D G ^a   Khaw, K T ^a   Schottker B ^a   Muller H ^a   Rothenbacher, D ^a   Jansson, J H ^a   Wennberg, P ^a   Nissinen, A ^a   Donfrancesco, C ^a   Salomaa, V ^a   Harald, K ^a   Jousilahti, P ^a   Vartiainen, E ^a   Woodward, M ^a   D'Agostino, R B ^a   Wolf, P A ^a   Vasan, R S ^a   Pencina, M J ^a   Bladbjerg, E M ^a   Jorgensen T ^a   Moller L ^a   Jespersen, J ^a   Dankner, R ^a   Chetrit, A ^a   Lubin, F ^a   Rosengren, A ^a   Lappas, G ^a   Eriksson, H ^a   Bjorkelund C ^a   Lissner, L ^a   Bengtsson, C ^a   Nagel, D ^a   Kiyohara, Y ^a   Arima, H ^a   Doi, Y ^a   Ninomiya, T ^a   Rodriguez, B ^a   Dekker, J M ^a   Nijpels, G ^a   Stehouwer, C D A ^a   Iso, H ^a,a   Kitamura, A ^a,a   Yamagishi, K ^a   Noda, H ^a   Goldbourt, U ^a   Kauhanen, J ^a   Salonen, J T ^a   Cooper, J A ^a,a   Verschuren, W M M ^a,a   Blokstra, A ^a   Shea, S ^a   Doring A ^a   Meisinger, C ^a   Bueno de Mesquita, H B ^a   Kuller, L H ^a   Grandits, G ^a   Gillum, R F ^a   Mussolino, M ^a   Bauer, K A ^a   Kirkland, S ^a   Shaffer, J ^a   Korin, M R ^a   Sato, S ^a   Amouyel, P ^a   Arveiler, D ^a   Evans, A ^a   Ferrieres J ^a   Schulte, H ^a   Assmann, G ^a   Westendorp, R G ^a   Buckley, B M ^a   Packard, C J ^a   Sattar, N ^a   Cantin, B ^a   Despres J P ^a   Dagenais, G R ^a   Barrett Connor, E ^a   Wingard, D L ^a   Bettencourt, R ^a   Gudnason, V ^a   Aspelund, T ^a   Sigurdsson, G ^a   Thorsson, B ^a   Witteman, J ^a   Kardys, I ^a   Tiemeier, H ^a   Hofman, A ^a   Tunstall Pedoe, H ^a   Tavendale, R ^a   Lowe, G D O ^a   Howard, B V ^a   Zhang, Y ^a   Best, L ^a   Umans, J ^a   Ben Shlomo, Y ^a   Davey Smith, G ^a   Njolstad I ^a   Wilsgaard, T ^a   Ingelsson, E ^a   Lind, L ^a   Giedraitis, V ^a   Lannfelt, L ^a   Gaziano, J M ^a   Stampfer, M ^a   Ridker, P M ^a   Wassertheil Smoller, S ^a   Manson, J E ^a   Marmot, M ^a   Clarke, R ^a   Fletcher, A ^a   Brunner, E ^a   Shipley, M ^a   Buring, J ^a   Shepherd, J ^a   Cobbe, S M ^a   Ford, I ^a   Robertson, M ^a   Marin Ibanez A ^a   Feskens, E J M ^a   Kromhout, D ^a   more..

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b MRC BIOSTATISTICS UNIT   (United Kingdom)

Author keywords

Cardiovascular disease; Competing risks; Cost effectiveness; Meta analysis; Net benefit; Screening strategies

Indexed keywords

HYDROXYMETHYLGLUTARYL COENZYME A REDUCTASE INHIBITOR;

AGE; ARTICLE; CARDIOVASCULAR DISEASE; CARDIOVASCULAR RISK; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; DISCRIMINATION; ECONOMIC EVALUATION; GENDER; GEOGRAPHIC DISTRIBUTION; HEALTH ECONOMICS; HUMAN; MATHEMATICAL MODEL; MATHEMATICAL PHENOMENA; MEDICAL DECISION MAKING; META ANALYSIS (TOPIC); METHODOLOGY; PRACTICE GUIDELINE; PREDICTION; PROGNOSIS; PROGNOSTIC MODEL; PUBLIC HEALTH; PUBLIC HEALTH IMPACT; RISK FACTOR; RISK RECLASSIFICATION; RISK REDUCTION;

EID: 84855240264     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4362     Document Type: Article

References (37)

1. Pyorala K, De Backer G, Graham I, et al. Prevention of coronary heart disease in clinical practice. Recommendations of the Task Force of the European Society of Cardiology, European Atherosclerosis Society and European Society of Hypertension. European Heart Journal 1994; 15:1300-31.
2. Harrell FE, Lee KL, Mark DB. Multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine 1996; 15:361-387. DOI: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4.
3. Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007; 115:928-935. DOI: 10.1161/CIRCULATIONAHA.106.672402.
4. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: The Reynolds risk score. Journal of the American Medical Association 2007; 297:611-9. DOI: 10.1001/jama.297.12.1376.
5. Pencina MJ, D'Agostino Sr RB, D'Agostino Jr RB, Vasan RS. Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Statistics in Medicine 2008; 27:157-172. DOI: 10.1002/sim.2929.
6. Chambless LE, Cummiskey CP, Cui G. Several methods to assess improvement in risk prediction models: Extension to survival analysis. Statistics in Medicine 2010; 30:22-38. DOI: 10.1002/sim.4026.
7. Steyerberg EW, Pencina MJ. Reclassification calculations with incomplete follow-up. Annals of Internal Medicine 2010; 152:195-196. DOI: 10.1059/0003-4819-152-3-201002020-00019.
8. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattane MW. Assessing the performance of prediction models: A framework for traditional and novel measures. Epidemiology 2010; 21:128-138. DOI: 10.1097/EDE.0b013e3181c30fb2.
9. Greenland S. The need for reorientation toward cost-effective prediction: Comments on 'Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyonD' by M. J. Pencina et al., Statistics in Medicine (DOI: 10.1002/sim.2929). Statistics in Medicine 2007; 27:199-206. DOI: 10.1002/sim.2995.
10. Pencina MJ, D'Agostino Sr RB, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Statistics in Medicine 2011; 30(1):11-21. DOI: 10.1002/sim.4085.
11. Gail MH, Pfeiffer RM. On criteria for evaluating models of absolute risk. Biostatistics 2005; 6:227-239. DOI: 10.1093/biostatistics/kxi005.
12. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Medical Decision Making 2006; 26:565-574. DOI: 10.1177/0272989X0602600601.
13. Vickers A, Cronin A, Elkin E, Gonen M. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers. BMC Medical Informatics and Decision Making 2008; 8:53. DOI: 10.1186/1472-6947-8-53.
14. Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. Journal of the Royal Statistical Society, Series A 2009; 172:729-748. DOI: 10.1111/j.1467-985X.2009.00592.x.
15. Baigent C, Keech A, Kearney PM, Blackwell L, Buck G, Pollicino C, Kirby A, Sourjina T, Peto R, Collins R, Simes R. Cholesterol Treatment Trialists' (CTT) Collaborators. Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90, 056 participants in 14 randomised trials of statins. Lancet 2005; 366:1267-78. DOI: 10.1016/S0140-6736(05)67394-1.
16. British Cardiac Society, British Hypertension Society, Diabetes UK, HEART UK, Primary Care Cardiovascular Society, The Stroke Association. JBS 2: Joint British Societies' guidelines on prevention of cardiovascular disease in clinical practice. Heart 2005; 91(S5):v1-v52. DOI: 10.1136/hrt.2005.079988.
17. The Emerging Risk Factors Collaboration. Analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. European Journal of Epidemiology 2007; 22:839-869. DOI: 10.1007/sl0654-007-9165-7.
18. Thiébaut A, Bénichou J. Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study. Statistics in Medicine 2004; 23:3803-3820. DOI: 10.1002/sim.2098.
19. Collett D. Modelling Survival Data in Medical Research, (2nd). Boca Raton: Chapman & Hall/CRC, 2003.
20. Devlin N, Parkin D. Does NICE have a cost effectiveness threshold and what other factors influence its decisions? Adiscrete choice analysis. Health Economics 2004; 13:437-52.
21. Royston P, Sauerbrei W. A new measure of prognostic separation in survival data. Statistics in Medicine 2004; 23:723-748. DOI: 10.1002/sim.1621.
22. White IR. Multivariate random-effects meta-analysis. Stata Journal 2009; 9:40-56.
23. Altman DG, De Stavola BL, Love SB, Stepniewska KA. Review of survival analyses published in cancer journals. British Journal of Cancer 1995; 72:511-518.
24. Wolbers M, Koller MT, Witteman JCM, Steyerberg EW. Prognostic models with competing risks: Methods and application to coronary risk prediction. Epidemiology 2009; 20(4):555-561. DOI: 10.1097/EDE.0b013e3181a39056.
25. Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics 1995; 51:524-532. DOI: 10.2307/2532940.
26. National Institute for Health and Clinical Excellence. Measuring effectiveness and cost-effectiveness: the QALY. Retrieved on 29/11/2010 from .
27. Altman DG. Systematic reviews in health care: Systematic reviews of evaluations of prognostic variables. British Medical Journal 2001; 323:224-228. DOI: 10.1136/bmj.323.7306.224.
28. Wald N, Law M. A strategy to reduce cardiovascular disease by more than 80%. British Medical Journal 2003; 326:1419. DOI: 10.1136/bmj.326.7404.
29. Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. American Heart Journal 1991; 121:293-8.
30. Sullivan LM, Massaro JM, D'Agostino Sr RB. Presentation of multivariate data for clinical use: the Framingham study risk score functions. Statistics in Medicine 2004; 23:1631-1660. DOI: 10.1002/sim.1742.
31. Woodward M, Brindle P, Tunstall-Pedoe H. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart 2007; 93(2):172-6.
32. Hippisley-Cox J, Coupland C, Vinagradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: a prospective open cohort study. British Medical Journal 2007; 335:136. DOI: 10.1136/bmj.39261.471806.55.
33. Shepherd J, Cobbe SM, Ford I, Isles CG, Lorimer AR, MacFarlane PW, McKillop JH, Packard CJ. Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. West of Scotland Coronary Prevention Study Group. The New England Journal of Medicine 1995; 333:1301-1307. DOI: 10.1056/NEJM199511163332001.
34. Shroufi A, Powles JW. Adherence and chemoprevention in major cardiovascular disease: a simulation study of the benefits of additional use of statins. Journal of Epidemiology and Community Health 2010; 64:109-113. DOI: 10.1136/jech.2009.091033.
35. Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. British Medical Journal 2010; 341:c6624. DOI: 10.1136/bmj.c6624.
36. Lawrenson R, Williams T, Farmer R. Clinical information for research; the use of general practice databases. Journal of Public Health Medicine 1999; 21:299-304. DOI: 10.1093/pubmed/21.3.299.
37. Kalia NK, Miller LG, Nasir K, et al. Visualizing coronary calcium is associated with improvements in adherence to statin therapy. Atherosclerosis 2006; 185:394-399.